CT Criteria Parser
Analyze eligibility criteria in ClinicalTrials.gov. Example input: nctid NCT05859269
CT Criteria Parser plugin is a powerful tool that analyzes eligibility criteria in ClinicalTrials.gov. It allows you to input the NCT ID and parse the criteria to identify medical keywords associated with patients. The plugin extracts medical terms related to demographics, diseases, diagnoses, condition severity, procedures, treatments, measurements, observations, medications, and medical history. By doing so, it creates two tables, one for the Inclusion Criteria and another for the Exclusion Criteria. Each table categorizes the medical terms appropriately and displays them in capitalized STRONG tag, providing a clear and concise summary of the criteria. Now, you can analyze Clinical Trials data more efficiently and quickly.
Learn how to use CT Criteria Parser effectively! Here are a few example prompts, tips, and the documentation of available commands.
Prompt 1: "Can you parse the inclusion criteria for this clinical trial?"
Prompt 2: "Please analyze the exclusion criteria of this trial and display the relevant medical terms."
Prompt 3: "I need you to identify the patient demographics mentioned in the eligibility criteria."
Prompt 4: "What are the medical procedures listed in the inclusion criteria?"
Prompt 5: "Find all the medications specified in the exclusion criteria."
Features and commands
|This command retrieves the eligibility criteria for a specific clinical trial from ClinicalTrials.gov. You need to provide the NCT ID of the trial as a parameter.|
|This command parses the inclusion criteria of a clinical trial, identifying and categorizing key medical terms and patient demographics.|
|This command parses the exclusion criteria of a clinical trial, identifying and categorizing key medical terms and patient demographics.|
|This command displays the parsed medical terms from the eligibility criteria. It generates two tables: one for the inclusion criteria and another for the exclusion criteria. Each table has two columns, where the first column represents the category of the medical term and the second column contains the original text with the parsed medical terms enclosed within square brackets and displayed in STRONG tags, capitalized.|